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Fraud is evolving, and enterprises are at increasing risk of significant financial losses. Cybercriminals are more proactive than ever, leveraging technology to exploit vulnerabilities in systems and processes. But how can organizations stay ahead of these threats? In a recent episode of the Risk Management Show, Shai Gabay, CEO of cybersecurity leader Trustmi, shared his journey and insights into combating social-engineered fraud using AI. Let’s dive into his key takeaways and actionable advice for protecting your organization.

The Anatomy of a Social Engineering Attack: More Than Just a Clever Email

Social engineering attacks have evolved far beyond the days of poorly written phishing emails. Today, these attacks outpace many traditional cyber threats in both volume and sophistication. In 2025 alone, social engineering attacks surged by 200%, with Business Email Compromise (BEC) and non-BEC methods rising sharply. According to recent data, 60% of incident response cases now involve social engineering-related data exposure, and a staggering 85% of attacks begin with an email. The human factor in cybersecurity—our trust, habits, and emotions—remains the universal weak link that cybercriminals relentlessly exploit.

Beyond the Inbox: Expanding Attack Vectors

While email remains the most common entry point, social engineering attacks now leverage a variety of channels and techniques. Business Email Compromise (BEC) is a favorite tactic, responsible for $2.77 billion in losses in 2024 alone. However, attackers are increasingly creative:

• Audio Deepfakes: AI-generated voice calls impersonate executives, pressuring employees to authorize urgent wire transfers.
• Fake CAPTCHAs and Login Pages: Sophisticated phishing sites mimic trusted platforms, tricking users into entering credentials.
• Urgent Payment Requests: Attackers pose as vendors or partners, exploiting established workflows and creating a false sense of urgency.

These methods bypass traditional security controls and even multi-factor authentication (MFA), targeting the human element at every step.

The Human Factor: The Weakest Link

Cybersecurity isn’t just about code and firewalls; it’s about people. Attackers know that emotional manipulation and urgency are powerful tools. They exploit psychological triggers such as:

• Authority: Messages appear to come from senior leaders or trusted partners.
• Urgency: Victims are pressured to act quickly, leaving little time for scrutiny.
• Empathy or Fear: Appeals to help a colleague in distress or avoid negative consequences.

One CFO recently shared a story that highlights this vulnerability. Her finance team followed every protocol “by the book,” yet still lost six figures to a convincing phone call. The attacker, using a deepfake voice, impersonated the CEO and demanded an immediate transfer. The aftermath was a mix of blame and denial—proof that even well-trained teams can fall prey to expertly crafted social engineering fraud.

Manual Processes: A Persistent Blind Spot

Despite advances in technology, many organizations—especially in finance—still rely on manual procedures. Employees are often trained to process transactions efficiently, but not necessarily to question unusual requests. This creates a fertile ground for social engineering attacks. As one cybersecurity expert noted:

“You’re not paying people to think, you’re paying them to process and work.”

This mindset, combined with high-pressure workflows, makes it easier for attackers to slip past even the most robust technical defenses.

Case in Point: Even Tech Giants Aren’t Immune

Social engineering fraud does not discriminate by size or sector. Google and Facebook famously lost over $100 million to a B2B fraud scheme that exploited fake invoices and forged communications. Attackers studied internal processes, mimicked trusted vendors, and used psychological tactics to manipulate employees into authorizing payments. This high-profile example underscores the reality: no organization is immune to the human factor in cybersecurity.

Why Social Engineering Attacks Are So Effective

Several factors contribute to the effectiveness of social engineering attacks:

1. Exploiting Trust: Attackers research targets and build believable stories, often using publicly available information.
2. Bypassing Security Controls: Social engineering sidesteps technical barriers by manipulating people, not systems.
3. Leveraging Technology: AI-powered deepfakes and automated phishing tools increase both scale and believability.
4. Targeting Manual Workflows: Attackers exploit gaps in manual approval processes and overworked staff.

With 60% of incidents involving social engineering exposure, and attack vectors growing more sophisticated each year, organizations must recognize that Human Factor Cybersecurity is as critical as any technical solution. Email security threats, BEC, and social engineering fraud are not just IT problems—they are business risks that demand a holistic, people-centered defense strategy.

Finance vs. Security: Collaboration or Chaos?

When it comes to fraud management and risk management finance security, the relationship between finance and cybersecurity teams is often more chaotic than collaborative. Despite the common narrative that cross-functional teams work seamlessly, the reality is far from ideal. According to recent research, 33% of organizations admit that gaps in collaboration between finance and security directly contributed to a recent fraud incident. Yet, most organizations still claim their teams are aligned and effective. This disconnect reveals a critical vulnerability: while everyone talks about collaboration, few organizations have the structures or processes to make it real.

Organizational Silos: The Root of the Problem

One of the main challenges in collaboration finance security is the existence of organizational silos. In most B2B environments, the responsibility for fraud prevention is ambiguous. Unlike B2C companies, where fraud management is a well-defined science with dedicated teams and advanced technology, B2B organizations often rely on manual controls and unclear ownership. Typically, the CFO or finance department is expected to manage fraud risks, but their tools are usually limited to spreadsheets, manual checks, and basic awareness training. Technology adoption lags behind, and there is little innovation in fraud prevention methods.

Meanwhile, the cybersecurity team focuses on protecting digital assets, monitoring network security, and defending against technical threats. Their expertise lies in email security, intrusion detection, and incident response. However, when it comes to financial fraud—especially those involving social engineering attacks—these teams are often brought in only after an incident has occurred. There is rarely a proactive, shared approach to risk management finance security.

Manual Controls: A Recipe for Exploitation

The dominance of manual controls in finance departments creates a fertile ground for fraudsters. Attackers exploit the lack of automation, the absence of real-time monitoring, and the reliance on human judgment. For example, social engineering attacks such as business email compromise (BEC) often start with a phishing email. While cybersecurity teams may manage email security, they are not always aware of the nuances of financial workflows or the specific vulnerabilities in payment processes.

This gap is further widened by the fact that most B2B payment controls remain manual. There is little integration between financial systems and security monitoring tools. As a result, fraudsters can slip through the cracks, exploiting the lack of shared visibility and the slow, reactive nature of manual processes.

Ambiguous Ownership: Who Owns Fraud Prevention?

Another major issue is the lack of clear ownership. In many organizations, no single function is responsible for B2B fraud prevention. While the CFO may oversee financial controls, they rarely have the technical expertise to implement advanced security solutions. Conversely, the cybersecurity team may not have insight into financial processes or the authority to enforce controls in finance operations. This ambiguity leads to confusion, delayed responses, and missed opportunities to prevent fraud.

"Unlike B2C, where fraud management is a science, B2B environments often rely on manual controls and unclear ownership—leaving doors wide open for attackers."

Collaboration Gaps: The Fraudster’s Playground

These collaboration gaps are not just theoretical—they have real-world consequences. When finance and security teams fail to work together, attackers can exploit the lack of coordination. For example, a phishing email may bypass technical controls and land in a finance manager’s inbox. Without proper training, context, or real-time alerts from the security team, the finance manager may unknowingly authorize a fraudulent payment. This is why cross-functional collaboration is crucial for effective fraud prevention.

What’s Missing: Unified Structures and Shared Visibility

The solution is not just more procedures or better training. What’s needed is a structural change—a unified approach to risk management finance security. This means creating cross-functional teams where finance and security professionals work together, share information, and have joint ownership of fraud prevention. Imagine a fraud prevention SWAT team: accountants and cybersecurity experts collaborating over spreadsheets and network logs, analyzing suspicious activity in real time. While this may sound far-fetched, it is exactly what is needed to close the gaps that fraudsters exploit.

• Track and share financial loss data across teams, not just after incidents.
• Integrate security tools with financial workflows for real-time alerts and monitoring.
• Establish clear ownership for B2B fraud management, with shared accountability.
• Promote ongoing cross-functional collaboration through regular meetings and joint training.

Without these changes, organizations will continue to struggle with fragmented approaches, leaving them vulnerable to the human side of social engineering attacks. The chaos of siloed teams is a fraudster’s best friend; true collaboration is the only way forward.

AI in Fraud Detection: When Bots Battle Bots

The landscape of cyber fraud has changed dramatically, and the catalyst is artificial intelligence. Today, AI in Fraud Detection is not just a buzzword—it’s a necessity. As organizations experiment with generative AI for productivity and efficiency, cybercriminals are already wielding these same tools as powerful weapons. This digital arms race has created a world where bots battle bots, and the outcome hinges on which side leverages AI more effectively.

Generative AI has supercharged the capabilities of attackers. Gone are the days when phishing emails were riddled with typos or awkward phrasing. Now, fraudsters deploy AI to craft emails with flawless grammar, perfect context, and even mimic the tone and style of legitimate vendors or executives. Deepfake technology has added another layer of deception, enabling criminals to clone voices and faces for phone calls and video meetings. The result? Social engineering attacks that are nearly indistinguishable from genuine business communications.

Consider the classic example of the Google and Facebook financial scam, where attackers compromised a supplier in Taiwan and tricked both tech giants into changing bank details—resulting in a loss of $100 million. While the scam itself wasn’t new, the sophistication with which it was executed was unprecedented. Fast forward to today, and the stakes are even higher. Generative AI Cyber Crime has made it possible for attackers to orchestrate context-rich, highly convincing schemes at scale.

Traditionally, organizations have relied on manual controls to prevent fraud. The “call-back” procedure—where a finance team member verifies a payment request by phone—was once considered a gold standard. But this method is rapidly losing its edge. In a world where staff turnover is high and vendor contacts change frequently, verifying the right person is already a challenge. Add AI-powered voice cloning and deepfake calls into the mix, and even the most diligent employee can be fooled. Manual verification simply cannot keep up with the speed, scale, and sophistication of modern attacks.

This is where AI Impact Cyber Fraud in a positive sense: by empowering defenders with the same technological prowess as attackers. Companies like TrustMe Cybersecurity Solutions are leading the charge, using AI to connect the dots across disparate systems—email, accounts payable, procurement, and more. Instead of relying on a single point of verification, TrustMe integrates data from every step of the process. Their AI models analyze this data to detect subtle anomalies in payment requests, vendor details, and communication patterns.

The heart of this approach is Payment Anomaly Detection. By leveraging AI, TrustMe can flag suspicious transactions before the money leaves the organization. Their system assigns a simple risk score—green for safe, yellow for caution, and red for high risk—making it easy for finance teams to act quickly without disrupting their workflow. This seamless integration is crucial; after all, no business wants to overhaul its processes for the sake of security. TrustMe’s solution becomes a co-pilot during payment cycles, quietly monitoring and alerting when something doesn’t add up.

It’s important to recognize that AI is both the villain and the hero in this cyber saga. On one side, generative AI enables fraudsters to launch attacks that are faster, smarter, and harder to detect. On the other, AI-driven anomaly detection is emerging as the best practice for defending against these threats. The digital battlefield is constantly evolving, with no “final boss” in sight—just an ongoing arms race where adaptation is key.

Since 2024, the risks posed by AI-driven cyber fraud have risen sharply. Manual verification procedures, once reliable, now often fail against advanced AI attacks. The only way forward is to fight fire with fire: organizations must adopt AI in fraud detection as a core part of their defense strategy. By connecting systems, analyzing patterns, and acting before damage is done, AI offers a fighting chance in a world where bots are both the attackers and the defenders.

In conclusion, the human side of social engineering remains critical, but code and firewalls alone are no longer enough. The future of cybersecurity lies in harnessing AI—not just to keep pace with cybercriminals, but to stay one step ahead in the battle where bots face off against bots.

TL;DR: Fighting modern social engineering attacks requires more than just firewalls and antivirus—success hinges on cross-team collaboration, leveraging AI, and acknowledging the human element that cybercriminals so effectively exploit.